Predictive models of forest dynamics

被引：262

作者：

Purves, Drew ^{[1
]}

Pacala, Stephen ^{[2
]}

机构：

[1] Microsoft Res, Computat Ecol & Environm Sci Grp, Cambridge, England

[2] Princeton Univ, Dept Ecol & Evolut Biol, Princeton, NJ 08544 USA

来源：

SCIENCE | 2008年 / 320卷 / 5882期

关键词：

D O I：

10.1126/science.1155359

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Dynamic global vegetation models (DGVMs) have shown that forest dynamics could dramatically alter the response of the global climate system to increased atmospheric carbon dioxide over the next century. But there is little agreement between different DGVMs, making forest dynamics one of the greatest sources of uncertainty in predicting future climate. DGVM predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated by recent advances in the mathematics of forest modeling, ecological understanding of diverse forest communities, and the availability of forest inventory data.

引用

页码：1452 / 1453

页数：3

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